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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Accurate and efficient description of interacting carriers in quantum nanostructures by selected configuration interaction and perturbation theory

In this paper, we present a method to accurately and efficiently calculate many-body states of interacting carriers in quantum nanostructures based on a combination of iterative selection of configurations and perturbation theory. This method enables investigations of large excitonic complexes and multielectron systems with near full configuration interaction accuracy, even though only a small subspace of the full many-body Hilbert space is sampled, thus saving orders of magnitudes in computational resources. Important advantages of this method are that the convergence is controlled by a single parameter, the threshold, and that ground and excited states can be treated on an equal footing. On the example of InAsP nanowire quantum dots described using a million-atom tight-binding approach, we demonstrate the extreme efficiency of the method by numerical studies of large excitonic complexes filling up to the fourth electronic shell. We find that the method generally converges fast as a function of the threshold, profiting from a significant acceleration due to the perturbative corrections. The role of the choice of single-particle basis states is discussed. It is found that the algorithm converges faster in the Hartree-Fock basis only for highly charged systems, where Coulomb repulsion dominates. Finally, based on the observation that second-order perturbative energy corrections only depend on off-diagonal elements of the many-body Hamiltonian, we present a way to accurately calculate many-body states that requires only a relatively small number of Coulomb matrix elements.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Quantum simulations in effective model spaces: Hamiltonian-learning variational quantum eigensolver using digital quantum computers and application to the Lipkin-Meshkov-Glick model

Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly-interacting many-body systems relevant to nuclear physics is however inefficient, even using ideal quantum computers. In addition, currently available NISQ-era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the non-commutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations. Here, the goal of the present study is to explore and utilize the non-commutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning. We introduce an iterative hybrid classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the $\log$ of the size of the effective model space, rather than the particle number. HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wavefunction, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cut-off as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other non-perturbative EFTs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Optimal Economic Dispatch and Load-Following Strategies for Nuclear Integrated Energy Systems

The need for distributed and adaptable energy resources that can handle the growing unpredictability in both supply and demand is rising as the power system continues to modernize. In order to satisfy those needs and maintain grid resilience, nuclear power plants can dynamically control their output, despite typically being used as baseload generators. By incorporating energy storage and renewable energy sources, nuclear integrated energy systems are designed to satisfy the electrical and thermal demands of different end-user applications while ensuring flexible power operation. These systems generate revenue by participating in both wholesale and ancillary services electricity markets, as well as commodity markets for various byproducts generated from coupled industrial processes. This study addresses the economic dispatch efficiency of a tightly coupled nuclear integrated energy system comprising a gigawatt-scale light water reactor, commercialized in the U.S., a high-temperature steam electrolysis unit, a district heating network, and specified electrical loads. To demonstrate the nuclear power plant’s flexibility within the day-ahead unit commitment and economic dispatch framework, while maintaining equilibrium even during periods of refueling outages, this paper develops a mixed-integer linear programming framework that models the subsystems and components of its nuclear steam supply system. A systematic comparative analysis of flexible versus baseload nuclear power plant operation under varying levels of renewable energy integration indicates that flexible operation enhances system profitability by more than 18% while also increasing energy storage utilization, improving reactor responsiveness to load fluctuations, and allowing for greater participation across numerous electricity markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Techno-economic assessment of electricity market potential for co-located hydro-floating PV systems

Abstract—Harnessing renewable energy from diverse sources is paramount for sustainable power systems. Recently, co-located floating PV (FPV) systems present an intriguing prospect in this context. These hybrid systems, blending hydro and solar power, may offer a more consistent electricity output and potential economic advantages. Yet, assessing their actual potential requires a comprehensive techno-economic assessment. In addition, probabilistic price forecasting has recently gained attention in electricity market because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. To this end, this paper embarks on a journey to elucidate the electricity market potential of co-located hydro-FPV systems in a probabilistic fashion to investigate the technological merits and economic viability of co-located hydro-FPV under different market structures. Our preliminary findings suggest that LCOE and payback metrics are sensitive not only to different markets but also to different solar incentives. Concurrently, we also observe that the payback period is generally faster with a production tax credit (PTC) than an investment tax credit (ITC). This assessment serves as a cornerstone for understanding the future prospects of co-located hydro-FPV systems in modern electricity markets.

13 HYDRO ENERGY↗

DOE OSTI2025

Bidding Curve Design for Hybrid Power Plants with Uncertain Solar Forecast

This paper presents a novel bidding curve design algorithm tailored for hybrid power plants (HPPs) to participate in the wholesale electricity market. Utilizing forecasts for photovoltaic (PV) generation and available battery power, our algorithm strategically computes the bidding curve to maximize HPP profit while adeptly managing the inherent uncertainty associated with PV power generation. In addition, the introduction of the penalty cost in HPP bidding curves provides the system operator a tool to effectively manage the system-level uncertainty that caused by HPPs. Numerical analysis through Monte Carlo simulations confirms that our bidding curve methodology outperforms the benchmark across various scenarios.

bidding curve↗

DOE OSTI2026

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗

DOE OSTI2024

MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

Extensive roll-out of electric vehicles (EVs) requires large-scale deployment of high-power EV Charging Stations (EVCSs). EVCSs are connected to the internet using Internetof- Things (IoT) such as smartphones, to improve the charging experience of users and increase their profitability. This paper studies the feasibility of demand-side cyberattacks launched on power grids via such internet-connected highpower EVCSs. The attack mechanism distorts power grid frequency and voltage, and has the potential to trigger systemwide outages. The case study, based on the power grid and EV deployment plans in Manhattan, New York, illustrates potential impacts of such attacks. The results show that such attacks will become feasible in Manhattan, New York in 2030, as EV adoption increases. Furthermore, the attacks in 2030 are feasible even compromising a single company’s EVCS server in Manhattan, New York. The attacks can cause line overloads and trip over-frequency protection relays, causing system-side blackout in the power grid in Manhattan, New York. The paper informs planning authorities and power grid operators involved with the roll-out of EV charging infrastructure about potential cyberthreats to power grids via manipulating internet-connected high-power EVCSs.

Acharya, Samrat S.↗

DOE OSTI2021

ATLAS toward the High Luminosity era: challenges on electronic systems

To maximize the physics reach, the Large Hadron Collider (LHC) plans to increase its instantaneous luminosity to $7.5×10^{34} cm^{-2} s^{-1}$ and deliver $3 - 4 ab^{-1}$ of data at a center-of-mass energy of 14 TeV. To profit from this high-luminosity LHC operation with a pile-up up to 200 inelastic collisions per bunch crossing, performance of the ATLAS detector needs to be maintained and in many systems, improved. The upgraded detector will have an unprecedented output data rate of up to 200 TB/s. Real-time processing of this large data volume in a short time period is extremely challenging. New sets of both front-end and back-end electronics are required for all sub-detectors. The large number of detector channels, huge volumes of input and output data, short time available to process and transmit data, harsh radiation environment, and the need of low power consumption all impose great challenges on the designs of electronic systems.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Providing Ancillary Services with Photovoltaic Generation in Multi- Timescale Grid Operation

With penetration levels of photovoltaic (PV) generation substantially increasing, electric power systems need more flexible resources that can provide ancillary services to mitigate the variability and uncertainty of the PV. On one hand, the increase in PV generation necessitates more flexible resources; on the other hand, because of its low operation cost, PV generation has been replacing conventional generation, which is currently the main flexible resource. Consequently, there is a trend to require renewable generation, including PV, to provide flexible ancillary services. This paper proposes a multi-timescale grid operation model considering the various control strategies of PV providing different ancillary services. Numerical case studies demonstrate that with PV providing both regulation reserve and primary frequency reserve, the system operating costs and PV curtailment will be reduced significantly. Results show that not only the system reliability can be improved but also PV profitability with PV providing more ancillary services.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Market Pricing and Settlements Analysis Considering Capacity Sharing and Reserve Substitutions of Operating Reserve Products

Electricity market pricing and settlement are the key signals for real-time dispatch and long-term investment decisions. Regional transmission operators (RTOs) in the U.S. adopt uniform pricing scheme, which is based on the marginal costs of supplying an incremental MW of electric services. The marginal cost of an electric service is highly dependent on the constraints in the pricing models of RTOs. A slight difference in constraint modeling of pricing model on energy and ancillary services could result in drastically different market clearing prices (MCPs), cleared reserve quantities, and associated revenue. RTOs in the U.S. have various market designs and assumptions in ancillary services modeling in capacity sharing and reserve substitutes. This paper examines four combination models of capacity sharing and reserve substitutes and analyzes the associated market implications. The numerical results present that 1) cascading reserve requirements have direct impact on reserve pricing schemes 2) both cascading reserve requirements and sharing capacity have significant impact on reserve MCPs and locational marginal prices, and thus result in drastically different reserve revenue, energy revenue, generation cost, and generation profit.

ancillary services↗

DOE OSTI2021

Transactive Electric Vehicle Agent: Design and Performance Evaluation

Transactive electric vehicle (TEV) agent enables the utilization of EVs’ flexible resources for the benefits of both utility and customer by active market participation. In this work, we propose a novel TEV agent optimization with a novel slider feature that models an individual customer’s willingness to participate in the transactive market. We characterize the customer comfort in EV’s context using a slider that provides flexibility to the customer to choose a trade-off between comfort and profit. Finally, the proposed optimization framework is used to investigate the comparative benefits of V2G technology compared to V1G.

day-ahead electricity market, electric vehicle, fl↗

DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗

DOE OSTI2021

Operation of Natural Gas Pipeline Networks With Storage Under Transient Flow Conditions

Here, we formulate a nonlinear optimal control problem for intraday operation of a natural gas pipeline network that includes storage reservoirs. The dynamics of compressible gas flow through pipes, compressors, reservoirs, and wells are considered. In particular, a reservoir is modeled as a rigid, hollow container that stores gas under isothermal conditions and uniform density, and a well is modeled as a vertical pipe. For each pipe, flow dynamics are described by a coupled partial differential equation (PDE) system in density and mass flux variables, with momentum dissipation modeled using the Darcy–Wiesbach friction approximation. Compressors are modeled as scaling up the pressure of gas between the inlet and outlet. The governing equations for all network components are spatially discretized and assembled into a nonlinear differential-algebraic equation (DAE) system, which synthesizes above-ground pipeline and subsurface reservoir dynamics into a single reduced-order model. We seek to maximize an objective function that quantifies economic profit and network efficiency subject to the flow equations and inequalities that represent operating limitations. The problem is solved using a primal–dual interior point solver, and the solutions are validated in computational experiments and simulations on several pipeline test networks to demonstrate the effectiveness of the proposed methodology.

03 NATURAL GAS↗

DOE OSTI2021

The Impacts of Distinct Flexibility Enhancements on the Value and Dynamics of Natural Gas Power Plant Operations

The rapid increase of renewable generation and its anticipated continued growth requires greater operational flexibility in modern power systems. Previous studies explored the addition of more flexible resources to improve system flexibility, but this may also be achieved at lower cost by enhancing existing power plants via flexibility retrofits. Potential flexibility upgrades to existing thermal plants include modifications that enable faster ramp rates, increase maximum load levels, decrease minimum load levels, and provide faster and lower cost startup operations. In this paper, we focus on improvements to existing natural gas combined cycle generators, and apply a unit commitment model to analyze the impact that each specific upgrade has on their operational dynamics and profitability from both the system perspective and from the asset owner perspective. We show that the value of each type of flexibility improvement varies depending on the capacity factor and usage pattern of the generator before the upgrade, and that the relative benefit of each type of flexibility varies. We demonstrate diminishing returns to increased flexibility when upgrading multiple generators simultaneously. Sensitivity tests for several shares of renewable generation, different generation mixes and different net load realizations confirm the generality of the results shown.

03 NATURAL GAS↗

DOE OSTI2020

A Learning-based Power Management Method for Networked Microgrids Under Incomplete Information

In this paper, we present a model-free reinforcement learning (RL)-based technique to solve the power management problem of networked microgrids (MGs), when the utility has only limited or no detailed knowledge of the MG asset models.The proposed method consists of two levels: at the first level, using an approximate RL strategy and aggregated data, a utility agent determines the locational price signals to maximize its profit under incomplete information on the MGs’ private data behind their points of common coupling (PCC). The proposed RL strategy enables the utility to adapt to changing system conditions and learn from previous experiences, while respecting the data ownership of the MGs. At the second level, the MGs receive the price signals to dispatch their generation/storage assets individually over look-ahead decision time window, while considering power flow constraints. The performance of the proposed RL technique has been verified under realistic operational scenarios.

Zhang, Qianzhi↗

DOE OSTI2022

Offering of Variable Resources in Regulation Markets with Performance Targets: An Analysis

To date, wind and solar power plants have mainly provided energy. Going forward, with the application of appropriate controls, they can offer additional grid services, such as regulating reserves. Additional grid services can present an opportunity for additional value streams to wind and solar power plants. However, the additional value might not be realized when operator-determined performance targets and settlement adjustments for the delivery of regulation capacity are disregarded. Additionally, this article studies the participation of variable resources in centralized regulation markets. We derive analytical results for regulation offers in terms of quantity and price considering that a variable resource does not want to risk being disqualified as a regulation provider, incurring losses, or foregoing more profitable opportunities. Our analysis suggests that U.S. operator-determined performance targets impose an upper bound on the quantity of a variable resource’s regulation offer; and settlement adjustments for the delivery of regulation affect the price at which variable resources offer and the calculations of opportunity costs in case of imperfect regulation delivery.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Multi-layered Energy Management Framework for Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, and Forecast Uncertainties

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy and yields optimal scheduling of the energy resources on a rolling horizon basis, thereby considering the long-term forecast errors. A lower dispatch layer (LDL) takes the short-term forecast errors into account during the real-time operation of the XFCS. Per the latest research, monthly demand charges can be as high as 90% of the total monthly bills for EV fast charging stations; to this end, this paper takes the first attempt at the reduction of demand charges cost by considering the trade-off between the energy cost and monthly demand charges. Contrasting literature, this work allocates an energy reserve in the BESS stored energy to deal with the impact of short-term forecast errors on the optimized real-time operation of the XFCS. Moreover, degradation modeling considers the trade-off between short-term benefits and long-term BESS life degradation. As a result, case studies and a comparative analysis prove the efficacy of the proposed framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Operational Simulation of Multi-Functional Charging Station for Sustainable Transportation

As transportation systems move toward electrification and decarbonization, multifunctional charging stations (MFCS) are emerging as key infrastructure for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper presents a simulation model of an MFCS that integrates solar photovoltaic (PV), wind power, battery storage, hydrogen (\mathrm{H}_{2}) production, dual-pressure \mathrm{H}_{2} storage, fuel cells, and dynamic grid interactions. The model simulates daily operations using 5 -minute resolution data to capture realtime variability in renewable energy (RE), demand, and electricity prices. A flexible dispatch algorithm dynamically allocates energy for EV charging, \mathrm{H}_{2} production, storage, and grid transactions while respecting system constraints. Results show that the MFCS effectively prioritizes RE usage, minimizes waste, meets diverse energy demands, and achieves net operational profit. The model serves as a valuable decision-support tool for designing and optimizing integrated clean energy hubs for zero-emission transportation.

Gbadegoye, Jeremiah [University of Tennessee, Knox↗